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Merge branch 'master' into worksplit-multigpu

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This commit is contained in:
Jedrzej Kosinski 2025-02-11 22:34:51 -06:00
commit d2504fb701
11 changed files with 66 additions and 45 deletions
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3
comfy/cli_args.py
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@ -191,3 +191,6 @@ if args.windows_standalone_build:
if args.disable_auto_launch:
args.auto_launch = False
if args.force_fp16:
args.fp16_unet = True

3
comfy/clip_model.py
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@ -104,7 +104,8 @@ class CLIPTextModel_(torch.nn.Module):
mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(-torch.finfo(x.dtype).max).triu_(1)
causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)
if mask is not None:
mask += causal_mask
else:

61
comfy/k_diffusion/sampling.py
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@ -1267,7 +1267,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
return x
@torch.no_grad()
def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None, cfg_pp=False):
def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@ -1289,53 +1289,60 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
for i in trange(len(sigmas) - 1, disable=disable):
if s_churn > 0:
gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
sigma_hat = sigmas[i] * (gamma + 1)
else:
gamma = 0
sigma_hat = sigmas[i]
if gamma > 0:
eps = torch.randn_like(x) * s_noise
x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
denoised = model(x, sigmas[i] * s_in, **extra_args)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
if callback is not None:
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
if sigmas[i + 1] == 0 or old_denoised is None:
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
if sigma_down == 0 or old_denoised is None:
# Euler method
if cfg_pp:
d = to_d(x, sigma_hat, uncond_denoised)
x = denoised + d * sigmas[i + 1]
d = to_d(x, sigmas[i], uncond_denoised)
x = denoised + d * sigma_down
else:
d = to_d(x, sigma_hat, denoised)
dt = sigmas[i + 1] - sigma_hat
d = to_d(x, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
x = x + d * dt
else:
# Second order multistep method in https://arxiv.org/pdf/2308.02157
t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigmas[i + 1]), t_fn(sigmas[i - 1])
t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
h = t_next - t
c2 = (t_prev - t) / h
phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0)
b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0)
b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
if cfg_pp:
x = x + (denoised - uncond_denoised)
x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
else:
x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
x = (sigma_fn(t_next) / sigma_fn(t)) * x + h * (b1 * denoised + b2 * old_denoised)
# Noise addition
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
old_denoised = denoised
if cfg_pp:
old_denoised = uncond_denoised
else:
old_denoised = denoised
return x
@torch.no_grad()
def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=False)
def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
@torch.no_grad()
def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
@torch.no_grad()
def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
@torch.no_grad()
def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
@torch.no_grad()
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):

2
comfy/ldm/flux/math.py
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@ -22,7 +22,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
assert dim % 2 == 0
if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu():
if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
device = torch.device("cpu")
else:
device = pos.device

5
comfy/ldm/lumina/model.py
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@ -6,6 +6,7 @@ from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import comfy.ldm.common_dit
from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
from comfy.ldm.modules.attention import optimized_attention_masked
@ -594,6 +595,8 @@ class NextDiT(nn.Module):
t = 1.0 - timesteps
cap_feats = context
cap_mask = attention_mask
bs, c, h, w = x.shape
x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
"""
Forward pass of NextDiT.
t: (N,) tensor of diffusion timesteps
@ -613,7 +616,7 @@ class NextDiT(nn.Module):
x = layer(x, mask, freqs_cis, adaln_input)
x = self.final_layer(x, adaln_input)
x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)
x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
return -x

3
comfy/model_base.py
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@ -166,9 +166,6 @@ class BaseModel(torch.nn.Module):
def get_dtype(self):
return self.diffusion_model.dtype
def is_adm(self):
return self.adm_channels > 0
def encode_adm(self, **kwargs):
return None

20
comfy/model_management.py
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@ -266,6 +266,12 @@ if ENABLE_PYTORCH_ATTENTION:
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
try:
if is_nvidia() and args.fast:
torch.backends.cuda.matmul.allow_fp16_accumulation = True
except:
pass
try:
if int(torch_version[0]) == 2 and int(torch_version[2]) >= 5:
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
@ -281,15 +287,10 @@ elif args.highvram or args.gpu_only:
vram_state = VRAMState.HIGH_VRAM
FORCE_FP32 = False
FORCE_FP16 = False
if args.force_fp32:
logging.info("Forcing FP32, if this improves things please report it.")
FORCE_FP32 = True
if args.force_fp16:
logging.info("Forcing FP16.")
FORCE_FP16 = True
if lowvram_available:
if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
vram_state = set_vram_to
@ -1019,6 +1020,13 @@ def is_device_mps(device):
def is_device_cuda(device):
return is_device_type(device, 'cuda')
def is_directml_enabled():
global directml_enabled
if directml_enabled:
return True
return False
def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
global directml_enabled
@ -1026,7 +1034,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
if is_device_cpu(device):
return False
if FORCE_FP16:
if args.force_fp16:
return True
if FORCE_FP32:

3
comfy/samplers.py
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@ -879,7 +879,8 @@ class Sampler:
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "gradient_estimation"]
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
"gradient_estimation"]
class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

2
comfy/utils.py
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@ -58,7 +58,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
if "HeaderTooLarge" in message:
raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
if "MetadataIncompleteBuffer" in message:
raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
raise e
else:
if safe_load or ALWAYS_SAFE_LOAD:

6
nodes.py
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@ -924,7 +924,7 @@ class CLIPLoader:
CATEGORY = "advanced/loaders"
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl"
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl\nlumina2: gemma 2 2B"
def load_clip(self, clip_name, type="stable_diffusion", device="default"):
if type == "stable_cascade":
@ -1065,10 +1065,10 @@ class StyleModelApply:
(txt, keys) = t
keys = keys.copy()
# even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it
if strength_type == "attn_bias" and strength != 1.0 and "attention_mask" not in keys:
if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0):
# math.log raises an error if the argument is zero
# torch.log returns -inf, which is what we want
attn_bias = torch.log(torch.Tensor([strength]))
attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0]))
# get the size of the mask image
mask_ref_size = keys.get("attention_mask_img_shape", (1, 1))
n_ref = mask_ref_size[0] * mask_ref_size[1]

3
server.py
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@ -150,7 +150,8 @@ class PromptServer():
PromptServer.instance = self
mimetypes.init()
mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
mimetypes.add_type('application/javascript; charset=utf-8', '.js')
mimetypes.add_type('image/webp', '.webp')
self.user_manager = UserManager()
self.model_file_manager = ModelFileManager()